Efficient model-based 3D tracking of hand articulations using Kinect

By I. Oikonomidis, Nikolaos Kyriazis, Antonis A. Argyros. 2011

1,043 citations recorded by the source corpus.

Subfields: Computer Vision and Pattern Recognition, Control and Systems Engineering, Human-Computer Interaction

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Citations in the corpus

RGB-D datasets using microsoft kinect or similar sensors: a survey (2016, methodology): “This device equipped with intelligent algorithms is contributing to various applications, such as 3D-simultaneous localization and mapping (SLAM) [39, 54], people tracking [69], object recognition [11] and human activity analysis [13, 57], etc.”
Cross-Modal Deep Variational Hand Pose Estimation (2018, background): “The complex and dexterous articulation of the human hand is difﬁcult to model directly with geometric or physical constraints [18, 30, 33].”
Cascaded hand pose regression (2015, background): “Such high accuracy is even comparable to the stateof-the-art model based tracking approaches [15, 32, 23, 26] but may indicate that the dataset is too simple.”
Training a Feedback Loop for Hand Pose Estimation (2015, methodology): “The runtime of our method compares favorably with other model-based methods ranging between 12 and 60 fps [19, 22, 26, 29, 37].”
3D Hand Shape and Pose Estimation From a Single RGB Image (2019, background): “In the past decade, we have witnessed a rapid advance in 3D hand pose estimation from depth images [35, 52, 11, 14, 13, 61, 10, 15].”
Realtime and Robust Hand Tracking from Depth (2014, methodology): “Previous gradient based optimization for body tracking [27] and stochastic optimization for hand tracking [8, 9] cannot minimize the cost well, being either too sensitive to local optima or too slow in convergence.”
Depth-Based Hand Pose Estimation: Methods, Data, and Challenges (2015, neutral): “Speciﬁcally, we convert depth map measurements into a 3D voxel grid, and simultaneously detect and estimate pose by scanning over this grid with volumetric exemplar templates.”
A hand pose tracking benchmark from stereo matching (2017, methodology): “To evaluate hand tracking from stereo sequences, we implement two hand pose tracking methods [3, 17].”
Spatial Attention Deep Net with Partial PSO for Hierarchical Hybrid Hand Pose Estimation (2016, background): “φ0 = {q, x, y, z}, where q is a 4-D unit quaternion [1,3] representing the global rotation, [x, y, z] is the global location of the whole hand.”
Full DOF tracking of a hand interacting with an object by modeling occlusions and physical constraints (2011, methodology): “In our work presented in [17], we proposed an efficient (15 fps) method for tracking the articulation of a hand, which depends on visual input provided by the Kinect sensor [14].”
Real-Time Hand Tracking under Occlusion from an Egocentric RGB-D Sensor (2017, methodology): “Methods that use generative pose tracking have been successful for free hand tracking with only an RGB-D stream [19, 22, 35, 37].”
Weakly-Supervised 3D Hand Pose Estimation from Monocular RGB Images (2018, background): “In general, they can be classified into generative approaches [20, 34, 16], discriminative approaches [13, 40, 6, 7, 5, 8] and hybrid approaches [25, 31, 30].”
Expressive Body Capture: 3D Hands, Face, and Body From a Single Image (2019, background): “There is also an extensive literature on modelling hands [39, 53, 57, 58, 68, 69, 71, 74, 75] and faces [4, 9, 11, 13, 14, 43, 63, 76, 79] in 3D but in isolation from the rest of the body.”
A Survey on Human Motion Analysis from Depth Data (2013, background): “A very important cue is depth [118, 119] that has been recently revisited in the context of depth sensors [120].”
Real-Time Articulated Hand Pose Estimation Using Semi-supervised Transductive Regression Forests (2013, methodology): “Existing state-of-the-arts resort to synthetic data [16], or model-based optimisation [8, 15].”
TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE 1 ModDrop (2018, neutral): “In parallel, tracking-based approaches are advancing quickly [25], [26].”
Depth-Based 3D Hand Pose Estimation: From Current Achievements to Future Goals (2017, methodology): “We use both standard error metrics [30, 37, 51] and new proposed metrics to provide further insights.”
Real time hand pose estimation using depth sensors (2011, background): “[12] present a solution that makes use of both depth and color images.”